Transferring Grasping Skills to Novel Instances by Latent Space Non-Rigid Registration

Abstract

Robots acting in open environments need to be able to handle novel objects. Based on the observation that objects within a category are often similar in their shapes and usage, we propose an approach for transferring grasping skills from known instances to novel instances of an object category. Correspondences between the instances are established by means of a non-rigid registration method that combines the Coherent Point Drift approach with subspace methods.The known object instances are modeled using a canonical shape and a transformation which deforms it to match the instance shape. The principle axes of variation of these deformations define a low-dimensional latent space. New instances can be generated through interpolation and extrapolation in this shape space. For inferring the shape parameters of an unknown instance, an energy function expressed in terms of the latent variables is minimized. Due to the class-level knowledge of the object, our method is able to complete novel shapes from partial views. Control poses for generating grasping motions are transferred efficiently to novel instances by the estimated non-rigid transformation.

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“…In this process, partially occluded shapes are reconstructed. The registration is robust against noise and misalignments to certain extent [19]. Fig.…”

Robust Grasp Planning Over Uncertain Shape Completions

“…Although many of these approaches originates from the computer vision perspective where the focus is on achieving better shape reconstruction, similar work have also been successfully applied in robotics. For example to facilitate robotic grasping by using symmetry [20], heuristics [21], or template matching methods [22]. However, these methods are only applicable for specific sets of objects.…”

Flexible Disaster Response of Tomorrow: Final Presentation and Evaluation of the CENTAURO System

“…Grasp generation is based on a non-rigid registration that incorporates class-specific information embedded in a lowdimensional shape space for the object category. To learn the shape space of a category, object point clouds are registered towards a single canonical instance of the category [25,26]. During inference, we optimize a shape that matches best the observed point cloud.…”